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A recommendation-audit Skill tests whether agents surface your work
Focus: Agents for Everyone
Date: October 27, 2026
Topics: Agent Skills, Recommendation tests, Metadata review
Evidence: Publisher Authored
Availability: Available
Summary: Every reports 2,484 simulated sessions testing which articles AI assistants recommend, then describes headline and description changes intended to improve retrieval. The study is not live traffic and cannot establish general ranking effects. A reusable workflow freezes reader questions, records citations and omissions, changes metadata narrowly, reruns the same tests and preserves editorial review.
Why it matters: Editorial teams can distinguish discoverability from persuasion: hold the query set constant, compare before-and-after recommendations, verify every cited article and keep only metadata that remains accurate for people. The Skill produces evidence without promising traffic gains.
Source: How to Get AI Agents to Recommend Your Articles